{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "ID: 212002094 Name: Opsin Mahmud **\"Heart Diseases & Parkinson Detection\"**\n",
        "\n"
      ],
      "metadata": {
        "id": "Oe646g2kFfsQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bAiG6X78sF4K"
      },
      "outputs": [],
      "source": [
        "# Import the necessary libraries.\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.datasets import make_classification\n",
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Preprocessing\n",
        "data = pd.read_csv('/content/heart.csv')\n",
        "print(data.head())\n",
        "print(data.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wcPDS8jF7-wH",
        "outputId": "448c4a85-bf72-45a0-fc5b-c5856090190d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
            "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
            "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
            "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
            "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
            "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
            "\n",
            "   ca  thal  target  \n",
            "0   0     1       1  \n",
            "1   0     2       1  \n",
            "2   0     2       1  \n",
            "3   0     2       1  \n",
            "4   0     2       1  \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.fillna(data.mean(), inplace=True)  # Example for filling missing values\n"
      ],
      "metadata": {
        "id": "s255rQ7j8JcN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "data_scaled = scaler.fit_transform(data)\n"
      ],
      "metadata": {
        "id": "OuflLk6y8PG0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Assuming data is a Pandas DataFrame and you have already loaded it\n",
        "data = pd.read_csv('/content/heart.csv')\n",
        "\n",
        "# Separate features and target column\n",
        "X = data.drop('target', axis=1)  # Replace 'target' with the actual target column name\n",
        "y = data['target']  # Replace 'target' with the actual target column name\n",
        "\n",
        "# Normalize the features using StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)  # X_scaled is a numpy array now\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n"
      ],
      "metadata": {
        "id": "ZD4b_GKv8R_c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "TD_v8Rb18xPD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Assuming 'data' is your Pandas DataFrame and you have a 'target' column\n",
        "\n",
        "# Load your data (replace 'path_to_dataset.csv' with your actual file path)\n",
        "# data = pd.read_csv('path_to_dataset.csv')\n",
        "\n",
        "# Example: Splitting data\n",
        "X = data.drop('target', axis=1)  # Replace 'target' with your actual target column name\n",
        "y = data['target']  # Replace 'target' with your actual target column name\n",
        "\n",
        "# Split into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Scale the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Train Linear Regression model\n",
        "linear_reg = LinearRegression()\n",
        "linear_reg.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = linear_reg.predict(X_test_scaled)\n",
        "\n",
        "# Plotting the actual vs predicted values\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Scatter plot of actual vs predicted values\n",
        "plt.scatter(y_test, y_pred, color='blue', label='Predicted vs Actual')\n",
        "\n",
        "# Plotting the line of best fit\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_pred), max(y_pred)], color='red', linewidth=2, label='Best Fit Line')\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Actual Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Linear Regression: Actual vs Predicted')\n",
        "plt.legend()\n",
        "\n",
        "# Show plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "W45cJY6j85YB",
        "outputId": "3a52286b-e39e-4789-cb9e-70d0dd1dfd2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Custom implementation of gradient descent\n",
        "def gradient_descent(X, y, theta, alpha, iterations):\n",
        "    m = len(y)\n",
        "    for i in range(iterations):\n",
        "        predictions = X.dot(theta)\n",
        "        errors = predictions - y\n",
        "        theta -= (alpha / m) * X.T.dot(errors)\n",
        "    return theta\n"
      ],
      "metadata": {
        "id": "sU_hlNW4-R0-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Custom implementation of Gradient Descent\n",
        "def gradient_descent(X, y, theta, alpha, iterations):\n",
        "    m = len(y)  # number of data points\n",
        "    cost_history = []  # list to store the cost at each iteration\n",
        "\n",
        "    for i in range(iterations):\n",
        "        predictions = X.dot(theta)  # predicted values\n",
        "        errors = predictions - y  # residual errors\n",
        "        gradient = (1/m) * X.T.dot(errors)  # compute the gradient\n",
        "        theta -= alpha * gradient  # update theta (weights)\n",
        "\n",
        "        # Compute and store the cost function value for each iteration\n",
        "        cost = (1/(2*m)) * np.sum(errors ** 2)\n",
        "        cost_history.append(cost)\n",
        "\n",
        "    return theta, cost_history\n",
        "\n",
        "# Example data\n",
        "# X: Features matrix with a column of 1's for the bias term (intercept)\n",
        "# y: Target values\n",
        "\n",
        "# Sample data (X = feature, y = target)\n",
        "# Replace this with your actual dataset\n",
        "X = np.array([[1, 1], [1, 2], [1, 3], [1, 4], [1, 5]])  # Add a column of 1's for the bias term\n",
        "y = np.array([1, 2, 1.3, 3.75, 2.25])  # Target values\n",
        "\n",
        "# Initialize parameters\n",
        "theta = np.zeros(X.shape[1])  # Initialize theta as zeros (same number of parameters as columns in X)\n",
        "alpha = 0.1  # Learning rate\n",
        "iterations = 1000  # Number of iterations for gradient descent\n",
        "\n",
        "# Run gradient descent\n",
        "theta_final, cost_history = gradient_descent(X, y, theta, alpha, iterations)\n",
        "\n",
        "# Output the final theta and cost history\n",
        "print(f'Final theta: {theta_final}')\n",
        "print(f'Final cost: {cost_history[-1]}')\n",
        "\n",
        "# Plotting the cost history to see how it converges\n",
        "plt.plot(range(iterations), cost_history, color='blue')\n",
        "plt.xlabel('Number of Iterations')\n",
        "plt.ylabel('Cost')\n",
        "plt.title('Gradient Descent Convergence')\n",
        "plt.show()\n",
        "\n",
        "# Predictions using the final theta\n",
        "predictions = X.dot(theta_final)\n",
        "\n",
        "# Plotting the data points and the regression line\n",
        "plt.scatter(X[:, 1], y, color='blue', label='Data points')  # X[:, 1] is the feature column (ignoring bias term)\n",
        "plt.plot(X[:, 1], predictions, color='red', label='Regression Line')\n",
        "plt.xlabel('Feature')\n",
        "plt.ylabel('Target')\n",
        "plt.title('Linear Regression Using Gradient Descent')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 962
        },
        "id": "mc6ivA4b-fH1",
        "outputId": "0855b699-bf50-4e96-c391-1f1bd85a2792"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Final theta: [0.78499998 0.42500001]\n",
            "Final cost: 0.27907500000000013\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "y_pred_knn = knn.predict(X_test)\n"
      ],
      "metadata": {
        "id": "uTQ8NM2v_cny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k7ZyLiFuB9QK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Load a dataset (e.g., Iris dataset)\n",
        "data = load_iris()\n",
        "X = data.data  # Features\n",
        "y = data.target  # Labels\n",
        "\n",
        "# Split the data into training and testing sets (80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize the KNN classifier with K=5\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "\n",
        "# Train the classifier on the training data\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "# Predict the labels for the test data\n",
        "y_pred_knn = knn.predict(X_test)\n",
        "\n",
        "# Evaluate the classifier's performance\n",
        "accuracy = accuracy_score(y_test, y_pred_knn)\n",
        "\n",
        "print(f'KNN Classification Accuracy: {accuracy * 100:.2f}%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtRiWXke_oua",
        "outputId": "8c6eea45-fa10-407d-b299-4f26b2aa60c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Classification Accuracy: 100.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train, y_train)\n",
        "y_pred_log_reg = log_reg.predict(X_test)\n"
      ],
      "metadata": {
        "id": "nJjiNB4U_zji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Load a dataset (Iris dataset)\n",
        "data = load_iris()\n",
        "X = data.data[:, :2]  # We take only the first two features for better visualization\n",
        "y = data.target\n",
        "\n",
        "# Split the data into training and testing sets (80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize the Logistic Regression model\n",
        "log_reg = LogisticRegression()\n",
        "\n",
        "# Train the model\n",
        "log_reg.fit(X_train, y_train)\n",
        "\n",
        "# Predict the test set labels\n",
        "y_pred_log_reg = log_reg.predict(X_test)\n",
        "\n",
        "# Evaluate the model accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred_log_reg)\n",
        "print(f'Logistic Regression Accuracy: {accuracy * 100:.2f}%')\n",
        "\n",
        "# Plotting the decision boundary\n",
        "\n",
        "# Create a mesh grid for plotting the decision boundary\n",
        "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
        "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
        "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))\n",
        "\n",
        "# Predict for each point in the mesh grid\n",
        "Z = log_reg.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "# Plot decision boundary and points\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.contourf(xx, yy, Z, alpha=0.3, cmap=plt.cm.RdYlBu)\n",
        "plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o', s=100, cmap=plt.cm.RdYlBu)\n",
        "\n",
        "# Highlight the test points\n",
        "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred_log_reg, edgecolors='r', marker='x', s=100, cmap=plt.cm.RdYlBu)\n",
        "\n",
        "plt.title('Logistic Regression Decision Boundary with Test Data')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.colorbar()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "id": "35q7uE7o_7t4",
        "outputId": "02690d42-99dc-47bf-e8f8-db6c667a9080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 90.00%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-57e2369dfa05>:46: UserWarning: You passed a edgecolor/edgecolors ('r') for an unfilled marker ('x').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
            "  plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred_log_reg, edgecolors='r', marker='x', s=100, cmap=plt.cm.RdYlBu)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nb = GaussianNB()\n",
        "nb.fit(X_train, y_train)\n",
        "y_pred_nb = nb.predict(X_test)\n"
      ],
      "metadata": {
        "id": "2pROlN3zAFFJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"/content/heart.csv\"\n",
        "data = pd.read_csv(url, header=None, names=column_names)\n",
        "url = \"/content/heart.csv\"\n",
        "data = pd.read_csv(url, header=None, names=column_names)\n",
        "\n"
      ],
      "metadata": {
        "id": "jT5c7-onAVE4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "# Load the heart disease dataset (local path or correct path in your environment)\n",
        "url = \"/content/heart.csv\"  # Use the correct path if running in Colab or local\n",
        "\n",
        "# Verify if the path exists and file is accessible (Optional)\n",
        "import os\n",
        "if not os.path.exists(url):\n",
        "    print(f\"File not found at {url}. Please check the path.\")\n",
        "else:\n",
        "    # Load the dataset into a DataFrame\n",
        "    # Use header=0 to correctly assign the first row as column names\n",
        "    column_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target']\n",
        "    data = pd.read_csv(url, names=column_names, header=0)  # Correctly handle the header\n",
        "\n",
        "    # View the first few rows of the dataset\n",
        "    print(\"First few rows of the dataset:\")\n",
        "    print(data.head())\n",
        "\n",
        "    # Step 1: Preprocess the data (check for missing values, encoding, etc.)\n",
        "    # Check for missing values\n",
        "    print(\"\\nChecking for missing values in the dataset:\")\n",
        "    print(data.isnull().sum())\n",
        "\n",
        "    # Step 2: Split data into features (X) and target (y)\n",
        "    X = data.drop('target', axis=1)\n",
        "    y = data['target']\n",
        "\n",
        "    # Step 3: Split the data into training and testing sets (80% training, 20% testing)\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "    # Step 4: Initialize and train the Naive Bayes classifier\n",
        "    nb = GaussianNB()\n",
        "\n",
        "    # Fit the Naive Bayes model (this part should work unless there's an issue with data)\n",
        "    nb.fit(X_train, y_train)\n",
        "\n",
        "    # Step 5: Predict the labels for the test set\n",
        "    y_pred_nb = nb.predict(X_test)\n",
        "\n",
        "    # Step 6: Evaluate the model performance\n",
        "    accuracy = accuracy_score(y_test, y_pred_nb)\n",
        "    print(f\"\\nAccuracy: {accuracy * 100:.2f}%\")\n",
        "\n",
        "    # Confusion matrix to show true vs. predicted values\n",
        "    cm = confusion_matrix(y_test, y_pred_nb)\n",
        "    print(\"\\nConfusion Matrix:\")\n",
        "    print(cm)\n",
        "\n",
        "    # Classification report to show precision, recall, and f1-score\n",
        "    report = classification_report(y_test, y_pred_nb)\n",
        "    print(\"\\nClassification Report:\")\n",
        "    print(report)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "35Khno_GA4K_",
        "outputId": "3f8ec681-3a8a-42f9-9bd0-4c3880e6b677"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First few rows of the dataset:\n",
            "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
            "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
            "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
            "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
            "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
            "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
            "\n",
            "   ca  thal  target  \n",
            "0   0     1       1  \n",
            "1   0     2       1  \n",
            "2   0     2       1  \n",
            "3   0     2       1  \n",
            "4   0     2       1  \n",
            "\n",
            "Checking for missing values in the dataset:\n",
            "age         0\n",
            "sex         0\n",
            "cp          0\n",
            "trestbps    0\n",
            "chol        0\n",
            "fbs         0\n",
            "restecg     0\n",
            "thalach     0\n",
            "exang       0\n",
            "oldpeak     0\n",
            "slope       0\n",
            "ca          0\n",
            "thal        0\n",
            "target      0\n",
            "dtype: int64\n",
            "\n",
            "Accuracy: 86.89%\n",
            "\n",
            "Confusion Matrix:\n",
            "[[26  3]\n",
            " [ 5 27]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.90      0.87        29\n",
            "           1       0.90      0.84      0.87        32\n",
            "\n",
            "    accuracy                           0.87        61\n",
            "   macro avg       0.87      0.87      0.87        61\n",
            "weighted avg       0.87      0.87      0.87        61\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a simple feed-forward neural network\n",
        "model = Sequential()\n",
        "model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32)\n",
        "y_pred_nn = model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "z_2eyK-2CBWL",
        "outputId": "3579088d-34a2-4941-dbe6-9dae219e508c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.5104 - loss: 7.4684\n",
            "Epoch 2/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5623 - loss: 3.2916 \n",
            "Epoch 3/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4909 - loss: 1.4245 \n",
            "Epoch 4/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5270 - loss: 0.8290 \n",
            "Epoch 5/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5983 - loss: 0.7867 \n",
            "Epoch 6/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6491 - loss: 0.6777 \n",
            "Epoch 7/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5576 - loss: 0.7196 \n",
            "Epoch 8/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6942 - loss: 0.6459 \n",
            "Epoch 9/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6203 - loss: 0.7395 \n",
            "Epoch 10/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6025 - loss: 0.6881 \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "# Build a simple feed-forward neural network with 2 hidden layers\n",
        "model = Sequential()\n",
        "\n",
        "# Input layer with 64 neurons and ReLU activation\n",
        "model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))\n",
        "\n",
        "# Hidden layer with 32 neurons and ReLU activation\n",
        "model.add(Dense(32, activation='relu'))\n",
        "\n",
        "# Output layer with 1 neuron and sigmoid activation for binary classification\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Compile the model using binary cross-entropy loss and adam optimizer\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Train the model, which performs forward and backward propagation\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred_nn = model.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdvU8Y_ME9RY",
        "outputId": "43704d8d-94fb-447c-ba7f-a81b71f22adf"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.5101 - loss: 0.7073\n",
            "Epoch 2/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7281 - loss: 0.6214 \n",
            "Epoch 3/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7835 - loss: 0.5583 \n",
            "Epoch 4/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8269 - loss: 0.4865 \n",
            "Epoch 5/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8176 - loss: 0.4683 \n",
            "Epoch 6/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8692 - loss: 0.3914 \n",
            "Epoch 7/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8441 - loss: 0.4047 \n",
            "Epoch 8/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8726 - loss: 0.3432 \n",
            "Epoch 9/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8717 - loss: 0.3553 \n",
            "Epoch 10/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8870 - loss: 0.3135 \n",
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_rnn = Sequential()\n",
        "model_rnn.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
        "model_rnn.add(LSTM(50))\n",
        "model_rnn.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model_rnn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "model_rnn.fit(X_train, y_train, epochs=10, batch_size=32)\n",
        "y_pred_rnn = model_rnn.predict(X_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyPCiAK9CqmH",
        "outputId": "c5fd4b0b-520d-4783-901a-a36d6cb8a052"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 28ms/step - accuracy: 0.4821 - loss: 0.6922\n",
            "Epoch 2/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - accuracy: 0.5922 - loss: 0.6793\n",
            "Epoch 3/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.6399 - loss: 0.6642\n",
            "Epoch 4/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6866 - loss: 0.6440\n",
            "Epoch 5/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - accuracy: 0.6276 - loss: 0.6507\n",
            "Epoch 6/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.6346 - loss: 0.6398\n",
            "Epoch 7/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.6352 - loss: 0.6361\n",
            "Epoch 8/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - accuracy: 0.6408 - loss: 0.6446\n",
            "Epoch 9/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - accuracy: 0.6636 - loss: 0.6093\n",
            "Epoch 10/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.6849 - loss: 0.6086\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:5 out of the last 5 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7cd0219d8820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r\u001b[1m1/2\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 644ms/step"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:6 out of the last 6 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x7cd0219d8820> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 620ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(X_train, y_train)\n",
        "y_pred_dt = dt.predict(X_test)\n"
      ],
      "metadata": {
        "id": "DZCtTkLCC0XY"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import tree\n",
        "\n",
        "# Load the dataset\n",
        "url = \"/content/heart.csv\"  # Update path as needed\n",
        "column_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target']\n",
        "data = pd.read_csv(url, names=column_names, header=0)\n",
        "\n",
        "# Check for missing values\n",
        "print(\"\\nChecking for missing values in the dataset:\")\n",
        "print(data.isnull().sum())\n",
        "\n",
        "# Split data into features (X) and target (y)\n",
        "X = data.drop('target', axis=1)\n",
        "y = data['target']\n",
        "\n",
        "# Standardize the feature set\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets (80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize and train the Decision Tree classifier\n",
        "dt = DecisionTreeClassifier(random_state=42)\n",
        "dt.fit(X_train, y_train)\n",
        "\n",
        "# Predict the labels for the test set\n",
        "y_pred_dt = dt.predict(X_test)\n",
        "\n",
        "# Evaluate the model performance\n",
        "accuracy = accuracy_score(y_test, y_pred_dt)\n",
        "print(f\"\\nAccuracy: {accuracy * 100:.2f}%\")\n",
        "\n",
        "# Confusion matrix to show true vs. predicted values\n",
        "cm = confusion_matrix(y_test, y_pred_dt)\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(cm)\n",
        "\n",
        "# Classification report to show precision, recall, and f1-score\n",
        "report = classification_report(y_test, y_pred_dt)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(report)\n",
        "\n",
        "# Visualize the decision tree (optional)\n",
        "plt.figure(figsize=(12, 8))\n",
        "tree.plot_tree(dt, filled=True, feature_names=X.columns, class_names=['No Disease', 'Disease'], rounded=True)\n",
        "plt.title(\"Decision Tree for Heart Disease Prediction\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7EQ5FI4OC9Em",
        "outputId": "652fabb6-758d-4af8-f2af-3161db871543"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Checking for missing values in the dataset:\n",
            "age         0\n",
            "sex         0\n",
            "cp          0\n",
            "trestbps    0\n",
            "chol        0\n",
            "fbs         0\n",
            "restecg     0\n",
            "thalach     0\n",
            "exang       0\n",
            "oldpeak     0\n",
            "slope       0\n",
            "ca          0\n",
            "thal        0\n",
            "target      0\n",
            "dtype: int64\n",
            "\n",
            "Accuracy: 75.41%\n",
            "\n",
            "Confusion Matrix:\n",
            "[[25  4]\n",
            " [11 21]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.86      0.77        29\n",
            "           1       0.84      0.66      0.74        32\n",
            "\n",
            "    accuracy                           0.75        61\n",
            "   macro avg       0.77      0.76      0.75        61\n",
            "weighted avg       0.77      0.75      0.75        61\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm = SVC(kernel='linear')\n",
        "svm.fit(X_train, y_train)\n",
        "y_pred_svm = svm.predict(X_test)\n"
      ],
      "metadata": {
        "id": "tJtTjyYBDNbu"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Load the dataset\n",
        "url = \"/content/heart.csv\"  # Update path as needed\n",
        "column_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target']\n",
        "data = pd.read_csv(url, names=column_names, header=0)\n",
        "\n",
        "# Check for missing values\n",
        "print(\"\\nChecking for missing values in the dataset:\")\n",
        "print(data.isnull().sum())\n",
        "\n",
        "# Split data into features (X) and target (y)\n",
        "X = data.drop('target', axis=1)\n",
        "y = data['target']\n",
        "\n",
        "# Standardize the feature set\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the data into training and testing sets (80% training, 20% testing)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize and train the Support Vector Machine (SVM) classifier with a linear kernel\n",
        "svm = SVC(kernel='linear', random_state=42)\n",
        "svm.fit(X_train, y_train)\n",
        "\n",
        "# Predict the labels for the test set\n",
        "y_pred_svm = svm.predict(X_test)\n",
        "\n",
        "# Evaluate the model performance\n",
        "accuracy = accuracy_score(y_test, y_pred_svm)\n",
        "print(f\"\\nAccuracy: {accuracy * 100:.2f}%\")\n",
        "\n",
        "# Confusion matrix to show true vs. predicted values\n",
        "cm = confusion_matrix(y_test, y_pred_svm)\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(cm)\n",
        "\n",
        "# Classification report to show precision, recall, and f1-score\n",
        "report = classification_report(y_test, y_pred_svm)\n",
        "print(\"\\nClassification Report:\")\n",
        "print(report)\n",
        "\n",
        "# Optional: Visualize the decision boundary (only works for 2D features)\n",
        "# For visualization, we will use just two features: age and cholesterol (chol)\n",
        "X_vis = X[['age', 'chol']]\n",
        "X_vis_scaled = scaler.fit_transform(X_vis)\n",
        "\n",
        "# Split the data again for visualization\n",
        "X_train_vis, X_test_vis, y_train_vis, y_test_vis = train_test_split(X_vis_scaled, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train the SVM again on these two features for visualization\n",
        "svm_vis = SVC(kernel='linear', random_state=42)\n",
        "svm_vis.fit(X_train_vis, y_train_vis)\n",
        "\n",
        "# Plot decision boundary for the first two features\n",
        "xx, yy = np.meshgrid(np.linspace(X_vis_scaled[:, 0].min(), X_vis_scaled[:, 0].max(), 100),\n",
        "                     np.linspace(X_vis_scaled[:, 1].min(), X_vis_scaled[:, 1].max(), 100))\n",
        "\n",
        "Z = svm_vis.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "plt.contourf(xx, yy, Z, alpha=0.75, cmap='coolwarm')\n",
        "plt.scatter(X_vis_scaled[:, 0], X_vis_scaled[:, 1], c=y, edgecolors='k', cmap='coolwarm')\n",
        "plt.title(\"SVM Decision Boundary (Age vs Cholesterol)\")\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Cholesterol\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0-1xdksiDYJG",
        "outputId": "e79e59e9-395d-4477-e7ec-766e592514bb"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Checking for missing values in the dataset:\n",
            "age         0\n",
            "sex         0\n",
            "cp          0\n",
            "trestbps    0\n",
            "chol        0\n",
            "fbs         0\n",
            "restecg     0\n",
            "thalach     0\n",
            "exang       0\n",
            "oldpeak     0\n",
            "slope       0\n",
            "ca          0\n",
            "thal        0\n",
            "target      0\n",
            "dtype: int64\n",
            "\n",
            "Accuracy: 86.89%\n",
            "\n",
            "Confusion Matrix:\n",
            "[[25  4]\n",
            " [ 4 28]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.86      0.86        29\n",
            "           1       0.88      0.88      0.88        32\n",
            "\n",
            "    accuracy                           0.87        61\n",
            "   macro avg       0.87      0.87      0.87        61\n",
            "weighted avg       0.87      0.87      0.87        61\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# If y_pred are probabilities, convert them to binary class labels (for models like Logistic Regression or Neural Networks)\n",
        "# For SVM, predictions are usually discrete, so this step is not necessary for SVM.\n",
        "# But to be sure, ensure your model output is a discrete label, for example:\n",
        "y_pred_class = (y_pred > 0.5).astype(int)  # Apply this only if y_pred is continuous\n",
        "\n",
        "# Now you can safely print the accuracy and confusion matrix\n",
        "print(f'Accuracy: {accuracy_score(y_test, y_pred_class):.2f}')\n",
        "print(f'Confusion Matrix:\\n {confusion_matrix(y_test, y_pred_class)}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2So2VLHjDrW1",
        "outputId": "fba04ff8-d987-409f-9ba9-2c396f948469"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.87\n",
            "Confusion Matrix:\n",
            " [[25  4]\n",
            " [ 4 28]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the heart disease dataset (adjust path if needed)\n",
        "url = \"/content/heart.csv\"  # Replace with your dataset path\n",
        "heart_data = pd.read_csv(url)\n",
        "\n",
        "# Display statistical summary of the dataset\n",
        "print(heart_data.describe())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0dhwA0fJH-T",
        "outputId": "0fd58f86-ed02-4dbe-a710-786252ce2bab"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              age         sex          cp    trestbps        chol         fbs  \\\n",
            "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
            "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
            "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
            "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
            "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
            "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
            "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
            "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
            "\n",
            "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
            "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
            "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
            "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
            "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
            "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
            "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
            "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
            "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
            "\n",
            "             thal      target  \n",
            "count  303.000000  303.000000  \n",
            "mean     2.313531    0.544554  \n",
            "std      0.612277    0.498835  \n",
            "min      0.000000    0.000000  \n",
            "25%      2.000000    0.000000  \n",
            "50%      2.000000    1.000000  \n",
            "75%      3.000000    1.000000  \n",
            "max      3.000000    1.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the distribution of Target Variable\n",
        "heart_data['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "qtMRAbZXJnkh",
        "outputId": "c9136e29-99f4-4657-933f-514ed3ed858a"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "1    165\n",
              "0    138\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 --> Defective Heart\n",
        "\n",
        "0 --> Healthy Heart"
      ],
      "metadata": {
        "id": "Qe8FhdnZJtT4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the Features and Target\n",
        "X = heart_data.drop(columns='target', axis=1)\n",
        "Y = heart_data['target']"
      ],
      "metadata": {
        "id": "AcayCU2WJrK5"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EYgx_qZoJzAA",
        "outputId": "4d5ba10e-7444-4afc-a988-6216cb0309bb"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
            "0     63    1   3       145   233    1        0      150      0      2.3   \n",
            "1     37    1   2       130   250    0        1      187      0      3.5   \n",
            "2     41    0   1       130   204    0        0      172      0      1.4   \n",
            "3     56    1   1       120   236    0        1      178      0      0.8   \n",
            "4     57    0   0       120   354    0        1      163      1      0.6   \n",
            "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
            "298   57    0   0       140   241    0        1      123      1      0.2   \n",
            "299   45    1   3       110   264    0        1      132      0      1.2   \n",
            "300   68    1   0       144   193    1        1      141      0      3.4   \n",
            "301   57    1   0       130   131    0        1      115      1      1.2   \n",
            "302   57    0   1       130   236    0        0      174      0      0.0   \n",
            "\n",
            "     slope  ca  thal  \n",
            "0        0   0     1  \n",
            "1        0   0     2  \n",
            "2        2   0     2  \n",
            "3        2   0     2  \n",
            "4        2   0     2  \n",
            "..     ...  ..   ...  \n",
            "298      1   0     3  \n",
            "299      1   0     3  \n",
            "300      1   2     3  \n",
            "301      1   1     3  \n",
            "302      1   1     2  \n",
            "\n",
            "[303 rows x 13 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9MQFr0LJ4XI",
        "outputId": "b052ddce-4139-4dae-e438-b2e2bea7c4be"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      1\n",
            "1      1\n",
            "2      1\n",
            "3      1\n",
            "4      1\n",
            "      ..\n",
            "298    0\n",
            "299    0\n",
            "300    0\n",
            "301    0\n",
            "302    0\n",
            "Name: target, Length: 303, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the Data into Training data & Test Data\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
      ],
      "metadata": {
        "id": "K9HhfpeNKPVX"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fSIbgAYKKYwm",
        "outputId": "f4b7714a-4448-4024-8a4c-b8f3cc38402a"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(303, 13) (242, 13) (61, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Fit the Logistic Regression model\n",
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(X_train, y_train)  # No need for epochs\n",
        "\n",
        "# Predict on training data\n",
        "X_train_prediction = log_reg.predict(X_train)\n",
        "\n",
        "# Calculate accuracy on training data\n",
        "training_data_accuracy = accuracy_score(y_train, X_train_prediction)\n",
        "\n",
        "print(f'Training Data Accuracy: {training_data_accuracy:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5NJmRuAKfpW",
        "outputId": "022f5d6e-09a4-4fe7-a9c1-fa2cb4848a55"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Accuracy: 0.59\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Define your neural network model\n",
        "model = Sequential()\n",
        "model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "\n",
        "# Fit the model\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32)\n",
        "\n",
        "# Predict on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "\n",
        "# If the model outputs probabilities, convert to binary labels\n",
        "X_train_prediction = (X_train_prediction > 0.5).astype(int)\n",
        "\n",
        "# Calculate accuracy on training data\n",
        "training_data_accuracy = accuracy_score(y_train, X_train_prediction)\n",
        "\n",
        "print(f'Training Data Accuracy: {training_data_accuracy:.2f}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWi9zoOmLJw1",
        "outputId": "97dc703a-b5ad-4c3a-dc13-d6c6aba97872"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.5339 - loss: 1.6684\n",
            "Epoch 2/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.4931 - loss: 0.9349 \n",
            "Epoch 3/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5099 - loss: 0.9012 \n",
            "Epoch 4/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5120 - loss: 0.8166 \n",
            "Epoch 5/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5794 - loss: 0.7979 \n",
            "Epoch 6/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5014 - loss: 0.7449 \n",
            "Epoch 7/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.5333 - loss: 0.8043 \n",
            "Epoch 8/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6175 - loss: 0.6744 \n",
            "Epoch 9/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6216 - loss: 0.7146 \n",
            "Epoch 10/10\n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.5490 - loss: 0.7133  \n",
            "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
            "Training Data Accuracy: 0.56\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import warnings\n",
        "\n",
        "# Suppress warnings\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        "\n",
        "# Input data\n",
        "input_data = (62, 0, 0, 140, 268, 0, 0, 160, 0, 3.6, 0, 2, 2)\n",
        "\n",
        "# Change the input data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# Reshape the numpy array as we are predicting for only one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)\n",
        "\n",
        "# Make a prediction\n",
        "prediction = model.predict(input_data_reshaped)\n",
        "print(prediction)\n",
        "\n",
        "# Interpret and print the prediction\n",
        "if prediction[0] == 0:\n",
        "    print(\"The Person does not have Heart Disease\")\n",
        "else:\n",
        "    print(\"The Person has Heart Disease\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDaRfmkPET-M",
        "outputId": "6cf8c485-85ef-4320-dcc2-f9b2284f4ea3"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "[[1.]]\n",
            "The Person has Heart Disease\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Apply parkinson Diseases Detection**"
      ],
      "metadata": {
        "id": "y5gRL8mDPsv_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the data from csv file to a Pandas DataFrame\n",
        "parkinsons_data = pd.read_csv('/content/parkinsons.csv')"
      ],
      "metadata": {
        "id": "nCdGqL2lP122"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# printing the first 5 rows of the dataframe\n",
        "parkinsons_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "XN7U-KCWP7Pe",
        "outputId": "7b4cbc57-4e11-4767-997c-fc1cada92a7b"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
              "0  phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
              "1  phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
              "2  phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
              "3  phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
              "4  phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
              "\n",
              "   MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
              "0           0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n",
              "1           0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n",
              "2           0.00009   0.00544   0.00781     0.01633       0.05233  ...   \n",
              "3           0.00009   0.00502   0.00698     0.01505       0.05492  ...   \n",
              "4           0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n",
              "\n",
              "   Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
              "0      0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
              "1      0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
              "2      0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
              "3      0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
              "4      0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
              "\n",
              "    spread2        D2       PPE  \n",
              "0  0.266482  2.301442  0.284654  \n",
              "1  0.335590  2.486855  0.368674  \n",
              "2  0.311173  2.342259  0.332634  \n",
              "3  0.334147  2.405554  0.368975  \n",
              "4  0.234513  2.332180  0.410335  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f00f4689-ace2-4714-a21b-c40e4edc2bdb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>MDVP:Fo(Hz)</th>\n",
              "      <th>MDVP:Fhi(Hz)</th>\n",
              "      <th>MDVP:Flo(Hz)</th>\n",
              "      <th>MDVP:Jitter(%)</th>\n",
              "      <th>MDVP:Jitter(Abs)</th>\n",
              "      <th>MDVP:RAP</th>\n",
              "      <th>MDVP:PPQ</th>\n",
              "      <th>Jitter:DDP</th>\n",
              "      <th>MDVP:Shimmer</th>\n",
              "      <th>...</th>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <th>NHR</th>\n",
              "      <th>HNR</th>\n",
              "      <th>status</th>\n",
              "      <th>RPDE</th>\n",
              "      <th>DFA</th>\n",
              "      <th>spread1</th>\n",
              "      <th>spread2</th>\n",
              "      <th>D2</th>\n",
              "      <th>PPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>phon_R01_S01_1</td>\n",
              "      <td>119.992</td>\n",
              "      <td>157.302</td>\n",
              "      <td>74.997</td>\n",
              "      <td>0.00784</td>\n",
              "      <td>0.00007</td>\n",
              "      <td>0.00370</td>\n",
              "      <td>0.00554</td>\n",
              "      <td>0.01109</td>\n",
              "      <td>0.04374</td>\n",
              "      <td>...</td>\n",
              "      <td>0.06545</td>\n",
              "      <td>0.02211</td>\n",
              "      <td>21.033</td>\n",
              "      <td>1</td>\n",
              "      <td>0.414783</td>\n",
              "      <td>0.815285</td>\n",
              "      <td>-4.813031</td>\n",
              "      <td>0.266482</td>\n",
              "      <td>2.301442</td>\n",
              "      <td>0.284654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>phon_R01_S01_2</td>\n",
              "      <td>122.400</td>\n",
              "      <td>148.650</td>\n",
              "      <td>113.819</td>\n",
              "      <td>0.00968</td>\n",
              "      <td>0.00008</td>\n",
              "      <td>0.00465</td>\n",
              "      <td>0.00696</td>\n",
              "      <td>0.01394</td>\n",
              "      <td>0.06134</td>\n",
              "      <td>...</td>\n",
              "      <td>0.09403</td>\n",
              "      <td>0.01929</td>\n",
              "      <td>19.085</td>\n",
              "      <td>1</td>\n",
              "      <td>0.458359</td>\n",
              "      <td>0.819521</td>\n",
              "      <td>-4.075192</td>\n",
              "      <td>0.335590</td>\n",
              "      <td>2.486855</td>\n",
              "      <td>0.368674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>phon_R01_S01_3</td>\n",
              "      <td>116.682</td>\n",
              "      <td>131.111</td>\n",
              "      <td>111.555</td>\n",
              "      <td>0.01050</td>\n",
              "      <td>0.00009</td>\n",
              "      <td>0.00544</td>\n",
              "      <td>0.00781</td>\n",
              "      <td>0.01633</td>\n",
              "      <td>0.05233</td>\n",
              "      <td>...</td>\n",
              "      <td>0.08270</td>\n",
              "      <td>0.01309</td>\n",
              "      <td>20.651</td>\n",
              "      <td>1</td>\n",
              "      <td>0.429895</td>\n",
              "      <td>0.825288</td>\n",
              "      <td>-4.443179</td>\n",
              "      <td>0.311173</td>\n",
              "      <td>2.342259</td>\n",
              "      <td>0.332634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>phon_R01_S01_4</td>\n",
              "      <td>116.676</td>\n",
              "      <td>137.871</td>\n",
              "      <td>111.366</td>\n",
              "      <td>0.00997</td>\n",
              "      <td>0.00009</td>\n",
              "      <td>0.00502</td>\n",
              "      <td>0.00698</td>\n",
              "      <td>0.01505</td>\n",
              "      <td>0.05492</td>\n",
              "      <td>...</td>\n",
              "      <td>0.08771</td>\n",
              "      <td>0.01353</td>\n",
              "      <td>20.644</td>\n",
              "      <td>1</td>\n",
              "      <td>0.434969</td>\n",
              "      <td>0.819235</td>\n",
              "      <td>-4.117501</td>\n",
              "      <td>0.334147</td>\n",
              "      <td>2.405554</td>\n",
              "      <td>0.368975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>phon_R01_S01_5</td>\n",
              "      <td>116.014</td>\n",
              "      <td>141.781</td>\n",
              "      <td>110.655</td>\n",
              "      <td>0.01284</td>\n",
              "      <td>0.00011</td>\n",
              "      <td>0.00655</td>\n",
              "      <td>0.00908</td>\n",
              "      <td>0.01966</td>\n",
              "      <td>0.06425</td>\n",
              "      <td>...</td>\n",
              "      <td>0.10470</td>\n",
              "      <td>0.01767</td>\n",
              "      <td>19.649</td>\n",
              "      <td>1</td>\n",
              "      <td>0.417356</td>\n",
              "      <td>0.823484</td>\n",
              "      <td>-3.747787</td>\n",
              "      <td>0.234513</td>\n",
              "      <td>2.332180</td>\n",
              "      <td>0.410335</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
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              "  <style>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
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              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "parkinsons_data"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# number of rows and columns in the dataframe\n",
        "parkinsons_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyTTs6biP_A1",
        "outputId": "7ee8e6f1-731d-477e-f665-17461a019da1"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(195, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting more information about the dataset\n",
        "parkinsons_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLdIAyxHQBf1",
        "outputId": "45451eca-959f-4a3f-d352-eb464f1a88ee"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 195 entries, 0 to 194\n",
            "Data columns (total 24 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   name              195 non-null    object \n",
            " 1   MDVP:Fo(Hz)       195 non-null    float64\n",
            " 2   MDVP:Fhi(Hz)      195 non-null    float64\n",
            " 3   MDVP:Flo(Hz)      195 non-null    float64\n",
            " 4   MDVP:Jitter(%)    195 non-null    float64\n",
            " 5   MDVP:Jitter(Abs)  195 non-null    float64\n",
            " 6   MDVP:RAP          195 non-null    float64\n",
            " 7   MDVP:PPQ          195 non-null    float64\n",
            " 8   Jitter:DDP        195 non-null    float64\n",
            " 9   MDVP:Shimmer      195 non-null    float64\n",
            " 10  MDVP:Shimmer(dB)  195 non-null    float64\n",
            " 11  Shimmer:APQ3      195 non-null    float64\n",
            " 12  Shimmer:APQ5      195 non-null    float64\n",
            " 13  MDVP:APQ          195 non-null    float64\n",
            " 14  Shimmer:DDA       195 non-null    float64\n",
            " 15  NHR               195 non-null    float64\n",
            " 16  HNR               195 non-null    float64\n",
            " 17  status            195 non-null    int64  \n",
            " 18  RPDE              195 non-null    float64\n",
            " 19  DFA               195 non-null    float64\n",
            " 20  spread1           195 non-null    float64\n",
            " 21  spread2           195 non-null    float64\n",
            " 22  D2                195 non-null    float64\n",
            " 23  PPE               195 non-null    float64\n",
            "dtypes: float64(22), int64(1), object(1)\n",
            "memory usage: 36.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for missing values in each column\n",
        "parkinsons_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "id": "mmK0cp8AQEym",
        "outputId": "38ede053-83d4-466c-da94-47c6e4c6cc7b"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name                0\n",
              "MDVP:Fo(Hz)         0\n",
              "MDVP:Fhi(Hz)        0\n",
              "MDVP:Flo(Hz)        0\n",
              "MDVP:Jitter(%)      0\n",
              "MDVP:Jitter(Abs)    0\n",
              "MDVP:RAP            0\n",
              "MDVP:PPQ            0\n",
              "Jitter:DDP          0\n",
              "MDVP:Shimmer        0\n",
              "MDVP:Shimmer(dB)    0\n",
              "Shimmer:APQ3        0\n",
              "Shimmer:APQ5        0\n",
              "MDVP:APQ            0\n",
              "Shimmer:DDA         0\n",
              "NHR                 0\n",
              "HNR                 0\n",
              "status              0\n",
              "RPDE                0\n",
              "DFA                 0\n",
              "spread1             0\n",
              "spread2             0\n",
              "D2                  0\n",
              "PPE                 0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Fo(Hz)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Fhi(Hz)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Flo(Hz)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Jitter(%)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Jitter(Abs)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:RAP</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:PPQ</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jitter:DDP</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Shimmer</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:Shimmer(dB)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shimmer:APQ3</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shimmer:APQ5</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MDVP:APQ</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NHR</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HNR</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RPDE</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DFA</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spread1</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spread2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D2</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PPE</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting some statistical measures about the data\n",
        "parkinsons_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "PddBQDB_QIEl",
        "outputId": "749329d2-19c4-4881-f9fa-b26babd8880d"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
              "count   195.000000    195.000000    195.000000      195.000000   \n",
              "mean    154.228641    197.104918    116.324631        0.006220   \n",
              "std      41.390065     91.491548     43.521413        0.004848   \n",
              "min      88.333000    102.145000     65.476000        0.001680   \n",
              "25%     117.572000    134.862500     84.291000        0.003460   \n",
              "50%     148.790000    175.829000    104.315000        0.004940   \n",
              "75%     182.769000    224.205500    140.018500        0.007365   \n",
              "max     260.105000    592.030000    239.170000        0.033160   \n",
              "\n",
              "       MDVP:Jitter(Abs)    MDVP:RAP    MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n",
              "count        195.000000  195.000000  195.000000  195.000000    195.000000   \n",
              "mean           0.000044    0.003306    0.003446    0.009920      0.029709   \n",
              "std            0.000035    0.002968    0.002759    0.008903      0.018857   \n",
              "min            0.000007    0.000680    0.000920    0.002040      0.009540   \n",
              "25%            0.000020    0.001660    0.001860    0.004985      0.016505   \n",
              "50%            0.000030    0.002500    0.002690    0.007490      0.022970   \n",
              "75%            0.000060    0.003835    0.003955    0.011505      0.037885   \n",
              "max            0.000260    0.021440    0.019580    0.064330      0.119080   \n",
              "\n",
              "       MDVP:Shimmer(dB)  ...  Shimmer:DDA         NHR         HNR      status  \\\n",
              "count        195.000000  ...   195.000000  195.000000  195.000000  195.000000   \n",
              "mean           0.282251  ...     0.046993    0.024847   21.885974    0.753846   \n",
              "std            0.194877  ...     0.030459    0.040418    4.425764    0.431878   \n",
              "min            0.085000  ...     0.013640    0.000650    8.441000    0.000000   \n",
              "25%            0.148500  ...     0.024735    0.005925   19.198000    1.000000   \n",
              "50%            0.221000  ...     0.038360    0.011660   22.085000    1.000000   \n",
              "75%            0.350000  ...     0.060795    0.025640   25.075500    1.000000   \n",
              "max            1.302000  ...     0.169420    0.314820   33.047000    1.000000   \n",
              "\n",
              "             RPDE         DFA     spread1     spread2          D2         PPE  \n",
              "count  195.000000  195.000000  195.000000  195.000000  195.000000  195.000000  \n",
              "mean     0.498536    0.718099   -5.684397    0.226510    2.381826    0.206552  \n",
              "std      0.103942    0.055336    1.090208    0.083406    0.382799    0.090119  \n",
              "min      0.256570    0.574282   -7.964984    0.006274    1.423287    0.044539  \n",
              "25%      0.421306    0.674758   -6.450096    0.174351    2.099125    0.137451  \n",
              "50%      0.495954    0.722254   -5.720868    0.218885    2.361532    0.194052  \n",
              "75%      0.587562    0.761881   -5.046192    0.279234    2.636456    0.252980  \n",
              "max      0.685151    0.825288   -2.434031    0.450493    3.671155    0.527367  \n",
              "\n",
              "[8 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d0ee68e-5968-4f20-a6c3-15aea51ece7c\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MDVP:Fo(Hz)</th>\n",
              "      <th>MDVP:Fhi(Hz)</th>\n",
              "      <th>MDVP:Flo(Hz)</th>\n",
              "      <th>MDVP:Jitter(%)</th>\n",
              "      <th>MDVP:Jitter(Abs)</th>\n",
              "      <th>MDVP:RAP</th>\n",
              "      <th>MDVP:PPQ</th>\n",
              "      <th>Jitter:DDP</th>\n",
              "      <th>MDVP:Shimmer</th>\n",
              "      <th>MDVP:Shimmer(dB)</th>\n",
              "      <th>...</th>\n",
              "      <th>Shimmer:DDA</th>\n",
              "      <th>NHR</th>\n",
              "      <th>HNR</th>\n",
              "      <th>status</th>\n",
              "      <th>RPDE</th>\n",
              "      <th>DFA</th>\n",
              "      <th>spread1</th>\n",
              "      <th>spread2</th>\n",
              "      <th>D2</th>\n",
              "      <th>PPE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "      <td>195.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>154.228641</td>\n",
              "      <td>197.104918</td>\n",
              "      <td>116.324631</td>\n",
              "      <td>0.006220</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.003306</td>\n",
              "      <td>0.003446</td>\n",
              "      <td>0.009920</td>\n",
              "      <td>0.029709</td>\n",
              "      <td>0.282251</td>\n",
              "      <td>...</td>\n",
              "      <td>0.046993</td>\n",
              "      <td>0.024847</td>\n",
              "      <td>21.885974</td>\n",
              "      <td>0.753846</td>\n",
              "      <td>0.498536</td>\n",
              "      <td>0.718099</td>\n",
              "      <td>-5.684397</td>\n",
              "      <td>0.226510</td>\n",
              "      <td>2.381826</td>\n",
              "      <td>0.206552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>41.390065</td>\n",
              "      <td>91.491548</td>\n",
              "      <td>43.521413</td>\n",
              "      <td>0.004848</td>\n",
              "      <td>0.000035</td>\n",
              "      <td>0.002968</td>\n",
              "      <td>0.002759</td>\n",
              "      <td>0.008903</td>\n",
              "      <td>0.018857</td>\n",
              "      <td>0.194877</td>\n",
              "      <td>...</td>\n",
              "      <td>0.030459</td>\n",
              "      <td>0.040418</td>\n",
              "      <td>4.425764</td>\n",
              "      <td>0.431878</td>\n",
              "      <td>0.103942</td>\n",
              "      <td>0.055336</td>\n",
              "      <td>1.090208</td>\n",
              "      <td>0.083406</td>\n",
              "      <td>0.382799</td>\n",
              "      <td>0.090119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>88.333000</td>\n",
              "      <td>102.145000</td>\n",
              "      <td>65.476000</td>\n",
              "      <td>0.001680</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.000680</td>\n",
              "      <td>0.000920</td>\n",
              "      <td>0.002040</td>\n",
              "      <td>0.009540</td>\n",
              "      <td>0.085000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.013640</td>\n",
              "      <td>0.000650</td>\n",
              "      <td>8.441000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.256570</td>\n",
              "      <td>0.574282</td>\n",
              "      <td>-7.964984</td>\n",
              "      <td>0.006274</td>\n",
              "      <td>1.423287</td>\n",
              "      <td>0.044539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>117.572000</td>\n",
              "      <td>134.862500</td>\n",
              "      <td>84.291000</td>\n",
              "      <td>0.003460</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.001660</td>\n",
              "      <td>0.001860</td>\n",
              "      <td>0.004985</td>\n",
              "      <td>0.016505</td>\n",
              "      <td>0.148500</td>\n",
              "      <td>...</td>\n",
              "      <td>0.024735</td>\n",
              "      <td>0.005925</td>\n",
              "      <td>19.198000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.421306</td>\n",
              "      <td>0.674758</td>\n",
              "      <td>-6.450096</td>\n",
              "      <td>0.174351</td>\n",
              "      <td>2.099125</td>\n",
              "      <td>0.137451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>148.790000</td>\n",
              "      <td>175.829000</td>\n",
              "      <td>104.315000</td>\n",
              "      <td>0.004940</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.002500</td>\n",
              "      <td>0.002690</td>\n",
              "      <td>0.007490</td>\n",
              "      <td>0.022970</td>\n",
              "      <td>0.221000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.038360</td>\n",
              "      <td>0.011660</td>\n",
              "      <td>22.085000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.495954</td>\n",
              "      <td>0.722254</td>\n",
              "      <td>-5.720868</td>\n",
              "      <td>0.218885</td>\n",
              "      <td>2.361532</td>\n",
              "      <td>0.194052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>182.769000</td>\n",
              "      <td>224.205500</td>\n",
              "      <td>140.018500</td>\n",
              "      <td>0.007365</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.003835</td>\n",
              "      <td>0.003955</td>\n",
              "      <td>0.011505</td>\n",
              "      <td>0.037885</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.060795</td>\n",
              "      <td>0.025640</td>\n",
              "      <td>25.075500</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.587562</td>\n",
              "      <td>0.761881</td>\n",
              "      <td>-5.046192</td>\n",
              "      <td>0.279234</td>\n",
              "      <td>2.636456</td>\n",
              "      <td>0.252980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>260.105000</td>\n",
              "      <td>592.030000</td>\n",
              "      <td>239.170000</td>\n",
              "      <td>0.033160</td>\n",
              "      <td>0.000260</td>\n",
              "      <td>0.021440</td>\n",
              "      <td>0.019580</td>\n",
              "      <td>0.064330</td>\n",
              "      <td>0.119080</td>\n",
              "      <td>1.302000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.169420</td>\n",
              "      <td>0.314820</td>\n",
              "      <td>33.047000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.685151</td>\n",
              "      <td>0.825288</td>\n",
              "      <td>-2.434031</td>\n",
              "      <td>0.450493</td>\n",
              "      <td>3.671155</td>\n",
              "      <td>0.527367</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 23 columns</p>\n",
              "</div>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-474cf56f-8b38-4a99-b410-9b77c1c9996b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# distribution of target Variable\n",
        "parkinsons_data['status'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "PYFyuTTRQK9d",
        "outputId": "371b08ce-3ba0-40a2-a526-971993a98e44"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status\n",
              "1    147\n",
              "0     48\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>status</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 --> Parkinson's Positive\n",
        "\n",
        "0 --> Healthy"
      ],
      "metadata": {
        "id": "oeFYSInTQOCG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(parkinsons_data.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2AUELUQRQPHt",
        "outputId": "0e801c1f-cac6-40a3-9f21-36a777501c27"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name                 object\n",
            "MDVP:Fo(Hz)         float64\n",
            "MDVP:Fhi(Hz)        float64\n",
            "MDVP:Flo(Hz)        float64\n",
            "MDVP:Jitter(%)      float64\n",
            "MDVP:Jitter(Abs)    float64\n",
            "MDVP:RAP            float64\n",
            "MDVP:PPQ            float64\n",
            "Jitter:DDP          float64\n",
            "MDVP:Shimmer        float64\n",
            "MDVP:Shimmer(dB)    float64\n",
            "Shimmer:APQ3        float64\n",
            "Shimmer:APQ5        float64\n",
            "MDVP:APQ            float64\n",
            "Shimmer:DDA         float64\n",
            "NHR                 float64\n",
            "HNR                 float64\n",
            "status                int64\n",
            "RPDE                float64\n",
            "DFA                 float64\n",
            "spread1             float64\n",
            "spread2             float64\n",
            "D2                  float64\n",
            "PPE                 float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data = parkinsons_data.select_dtypes(include=['number'])\n",
        "grouped = numeric_data.groupby(parkinsons_data['status']).mean()\n",
        "print(grouped)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dn4YD4AYQTVs",
        "outputId": "e45b186e-4139-43e4-b68c-4a93f31f26cd"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
            "status                                                            \n",
            "0        181.937771    223.636750    145.207292        0.003866   \n",
            "1        145.180762    188.441463    106.893558        0.006989   \n",
            "\n",
            "        MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n",
            "status                                                                   \n",
            "0               0.000023  0.001925  0.002056    0.005776      0.017615   \n",
            "1               0.000051  0.003757  0.003900    0.011273      0.033658   \n",
            "\n",
            "        MDVP:Shimmer(dB)  ...  Shimmer:DDA       NHR        HNR  status  \\\n",
            "status                    ...                                             \n",
            "0               0.162958  ...     0.028511  0.011483  24.678750     0.0   \n",
            "1               0.321204  ...     0.053027  0.029211  20.974048     1.0   \n",
            "\n",
            "            RPDE       DFA   spread1   spread2        D2       PPE  \n",
            "status                                                              \n",
            "0       0.442552  0.695716 -6.759264  0.160292  2.154491  0.123017  \n",
            "1       0.516816  0.725408 -5.333420  0.248133  2.456058  0.233828  \n",
            "\n",
            "[2 rows x 23 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped = parkinsons_data.select_dtypes(include=['number']).groupby(parkinsons_data['status']).mean()"
      ],
      "metadata": {
        "id": "PGckDMuhQWqd"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in parkinsons_data.columns:\n",
        "    parkinsons_data[col] = pd.to_numeric(parkinsons_data[col], errors='coerce')"
      ],
      "metadata": {
        "id": "FXIiEVgFQbIV"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter numeric columns\n",
        "numeric_data = parkinsons_data.select_dtypes(include=['number'])\n",
        "\n",
        "# Group by 'status' and calculate the mean\n",
        "grouped = numeric_data.groupby(parkinsons_data['status']).mean()\n",
        "\n",
        "print(grouped)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxoryYMwQea8",
        "outputId": "d355296f-9b4f-469f-fcbc-47602dad3f24"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
            "status                                                                  \n",
            "0        NaN   181.937771    223.636750    145.207292        0.003866   \n",
            "1        NaN   145.180762    188.441463    106.893558        0.006989   \n",
            "\n",
            "        MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
            "status                                                                  ...   \n",
            "0               0.000023  0.001925  0.002056    0.005776      0.017615  ...   \n",
            "1               0.000051  0.003757  0.003900    0.011273      0.033658  ...   \n",
            "\n",
            "        Shimmer:DDA       NHR        HNR  status      RPDE       DFA  \\\n",
            "status                                                                 \n",
            "0          0.028511  0.011483  24.678750     0.0  0.442552  0.695716   \n",
            "1          0.053027  0.029211  20.974048     1.0  0.516816  0.725408   \n",
            "\n",
            "         spread1   spread2        D2       PPE  \n",
            "status                                          \n",
            "0      -6.759264  0.160292  2.154491  0.123017  \n",
            "1      -5.333420  0.248133  2.456058  0.233828  \n",
            "\n",
            "[2 rows x 24 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Pre-Processing\n",
        "# Separating the features & Target\n",
        "\n",
        "X = parkinsons_data.drop(columns=['name','status'], axis=1)\n",
        "Y = parkinsons_data['status']"
      ],
      "metadata": {
        "id": "9kenxnvzQia8"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3fxP6HmQlZc",
        "outputId": "1443be2e-5191-4ad4-ce10-0c6e56c24dbb"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
            "0        119.992       157.302        74.997         0.00784   \n",
            "1        122.400       148.650       113.819         0.00968   \n",
            "2        116.682       131.111       111.555         0.01050   \n",
            "3        116.676       137.871       111.366         0.00997   \n",
            "4        116.014       141.781       110.655         0.01284   \n",
            "..           ...           ...           ...             ...   \n",
            "190      174.188       230.978        94.261         0.00459   \n",
            "191      209.516       253.017        89.488         0.00564   \n",
            "192      174.688       240.005        74.287         0.01360   \n",
            "193      198.764       396.961        74.904         0.00740   \n",
            "194      214.289       260.277        77.973         0.00567   \n",
            "\n",
            "     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n",
            "0             0.00007   0.00370   0.00554     0.01109       0.04374   \n",
            "1             0.00008   0.00465   0.00696     0.01394       0.06134   \n",
            "2             0.00009   0.00544   0.00781     0.01633       0.05233   \n",
            "3             0.00009   0.00502   0.00698     0.01505       0.05492   \n",
            "4             0.00011   0.00655   0.00908     0.01966       0.06425   \n",
            "..                ...       ...       ...         ...           ...   \n",
            "190           0.00003   0.00263   0.00259     0.00790       0.04087   \n",
            "191           0.00003   0.00331   0.00292     0.00994       0.02751   \n",
            "192           0.00008   0.00624   0.00564     0.01873       0.02308   \n",
            "193           0.00004   0.00370   0.00390     0.01109       0.02296   \n",
            "194           0.00003   0.00295   0.00317     0.00885       0.01884   \n",
            "\n",
            "     MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA      NHR     HNR      RPDE  \\\n",
            "0               0.426  ...   0.02971      0.06545  0.02211  21.033  0.414783   \n",
            "1               0.626  ...   0.04368      0.09403  0.01929  19.085  0.458359   \n",
            "2               0.482  ...   0.03590      0.08270  0.01309  20.651  0.429895   \n",
            "3               0.517  ...   0.03772      0.08771  0.01353  20.644  0.434969   \n",
            "4               0.584  ...   0.04465      0.10470  0.01767  19.649  0.417356   \n",
            "..                ...  ...       ...          ...      ...     ...       ...   \n",
            "190             0.405  ...   0.02745      0.07008  0.02764  19.517  0.448439   \n",
            "191             0.263  ...   0.01879      0.04812  0.01810  19.147  0.431674   \n",
            "192             0.256  ...   0.01667      0.03804  0.10715  17.883  0.407567   \n",
            "193             0.241  ...   0.01588      0.03794  0.07223  19.020  0.451221   \n",
            "194             0.190  ...   0.01373      0.03078  0.04398  21.209  0.462803   \n",
            "\n",
            "          DFA   spread1   spread2        D2       PPE  \n",
            "0    0.815285 -4.813031  0.266482  2.301442  0.284654  \n",
            "1    0.819521 -4.075192  0.335590  2.486855  0.368674  \n",
            "2    0.825288 -4.443179  0.311173  2.342259  0.332634  \n",
            "3    0.819235 -4.117501  0.334147  2.405554  0.368975  \n",
            "4    0.823484 -3.747787  0.234513  2.332180  0.410335  \n",
            "..        ...       ...       ...       ...       ...  \n",
            "190  0.657899 -6.538586  0.121952  2.657476  0.133050  \n",
            "191  0.683244 -6.195325  0.129303  2.784312  0.168895  \n",
            "192  0.655683 -6.787197  0.158453  2.679772  0.131728  \n",
            "193  0.643956 -6.744577  0.207454  2.138608  0.123306  \n",
            "194  0.664357 -5.724056  0.190667  2.555477  0.148569  \n",
            "\n",
            "[195 rows x 22 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KEtFJSyMQoC0",
        "outputId": "a333ec45-1da4-4363-b3e5-420536fca1f5"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0      1\n",
            "1      1\n",
            "2      1\n",
            "3      1\n",
            "4      1\n",
            "      ..\n",
            "190    0\n",
            "191    0\n",
            "192    0\n",
            "193    0\n",
            "194    0\n",
            "Name: status, Length: 195, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the data to training data & Test data\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ],
      "metadata": {
        "id": "YJapLEssQrW0"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s1nkm2sBQuv8",
        "outputId": "72fb6df7-4881-4c72-b5f3-a9a9f39529d6"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(195, 22) (156, 22) (39, 22)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Standardization\n",
        "scaler = StandardScaler()\n",
        "# StandardScaler(copy=True, with_mean=True, with_std=True)"
      ],
      "metadata": {
        "id": "dYKDT9WzQyas"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = scaler.fit_transform(X_train)"
      ],
      "metadata": {
        "id": "KoIWtgucQ0vM"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "J-nZXo0xQ3PT"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0pzyikNFQ6tz",
        "outputId": "dcfe3324-8635-44ad-c5c1-71fb083ba52c"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.63239631 -0.02731081 -0.87985049 ... -0.97586547 -0.55160318\n",
            "   0.07769494]\n",
            " [-1.05512719 -0.83337041 -0.9284778  ...  0.3981808  -0.61014073\n",
            "   0.39291782]\n",
            " [ 0.02996187 -0.29531068 -1.12211107 ... -0.43937044 -0.62849605\n",
            "  -0.50948408]\n",
            " ...\n",
            " [-0.9096785  -0.6637302  -0.160638   ...  1.22001022 -0.47404629\n",
            "  -0.2159482 ]\n",
            " [-0.35977689  0.19731822 -0.79063679 ... -0.17896029 -0.47272835\n",
            "   0.28181221]\n",
            " [ 1.01957066  0.19922317 -0.61914972 ... -0.716232    1.23632066\n",
            "  -0.05829386]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Ensure X and y are defined correctly, and split the data\n",
        "X = data.drop('target', axis=1)  # Features\n",
        "y = data['target']  # Target variable\n",
        "\n",
        "# Check if X_train and y_train have the same number of samples\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Check if the dimensions are consistent\n",
        "print(f\"X_train shape: {X_train.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape}\")\n",
        "\n",
        "# Model Training\n",
        "model = SVC(kernel='linear')  # Create the SVC model with a linear kernel\n",
        "model.fit(X_train, y_train)  # Train the model using the training data\n",
        "\n",
        "# Make predictions\n",
        "y_pred_svm = model.predict(X_test)  # Predict on the test set\n",
        "\n",
        "# Evaluate accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred_svm)\n",
        "print(f'Accuracy: {accuracy:.2f}')\n",
        "\n",
        "# Confusion Matrix\n",
        "cm = confusion_matrix(y_test, y_pred_svm)\n",
        "print(f'Confusion Matrix:\\n{cm}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-clwIxSQ987",
        "outputId": "6315628b-9226-4228-9934-e03c15673c3d"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (242, 13)\n",
            "y_train shape: (242,)\n",
            "Accuracy: 0.87\n",
            "Confusion Matrix:\n",
            "[[25  4]\n",
            " [ 4 28]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# training the SVM model with training data\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "H-QPNvfkRZR2",
        "outputId": "dae86cd7-634c-4f84-ab7d-ca271a4fb965"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Generate a synthetic dataset for demonstration (2 features for 2D plot)\n",
        "X, Y = make_classification(\n",
        "    n_samples=100, n_features=2, n_informative=2, n_redundant=0, random_state=42\n",
        ")\n",
        "\n",
        "# Standardize the data\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "\n",
        "# Split into training and testing data (simple split here)\n",
        "X_train, Y_train = X[:80], Y[:80]\n",
        "X_test, Y_test = X[80:], Y[80:]\n",
        "\n",
        "# Train the SVM model with a linear kernel\n",
        "model = SVC(kernel='linear', random_state=42)\n",
        "model.fit(X_train, Y_train)\n",
        "\n",
        "# Plotting the data and decision boundary\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Plot data points\n",
        "plt.scatter(X_train[:, 0], X_train[:, 1], c=Y_train, cmap='coolwarm', s=50, edgecolors='k', label=\"Train Data\")\n",
        "plt.scatter(X_test[:, 0], X_test[:, 1], c=Y_test, cmap='coolwarm', s=50, edgecolors='k', marker='*', label=\"Test Data\")\n",
        "\n",
        "# Plot decision boundary\n",
        "ax = plt.gca()\n",
        "xlim = ax.get_xlim()\n",
        "ylim = ax.get_ylim()\n",
        "\n",
        "# Create a mesh to plot the decision boundary\n",
        "xx, yy = np.meshgrid(\n",
        "    np.linspace(xlim[0], xlim[1], 50),\n",
        "    np.linspace(ylim[0], ylim[1], 50)\n",
        ")\n",
        "Z = model.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
        "Z = Z.reshape(xx.shape)\n",
        "\n",
        "# Plot the decision boundary\n",
        "plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='k')\n",
        "plt.contourf(xx, yy, Z, levels=[-1, 0, 1], alpha=0.3, colors=['blue', 'red'])\n",
        "\n",
        "# Additional plot configurations\n",
        "plt.title('SVM with Linear Kernel')\n",
        "plt.xlabel('Feature 1')\n",
        "plt.ylabel('Feature 2')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "o9-owvlyR7CF",
        "outputId": "a29ec043-fd01-4b7e-c915-ce72ec843273"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Evaluation"
      ],
      "metadata": {
        "id": "5SD7HBKISFvH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy score on training data\n",
        "# Accuracy Score\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)"
      ],
      "metadata": {
        "id": "5lY0afjISR5o"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Predict on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "\n",
        "# Calculate accuracy score on training data\n",
        "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)\n",
        "\n",
        "# Print accuracy score of training data (segmented output)\n",
        "print(\"Segment: Training Data Accuracy\")\n",
        "for i in range(5):\n",
        "    print(f\"Attempt {i + 1}: Accuracy score on training data: {training_data_accuracy:.2f}\")\n",
        "\n",
        "# Predict on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "\n",
        "# Calculate accuracy score on test data\n",
        "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)\n",
        "\n",
        "# Print accuracy score of test data\n",
        "print(\"\\nSegment: Test Data Accuracy\")\n",
        "for i in range(5):\n",
        "    print(f\"Attempt {i + 1}: Accuracy score on test data: {test_data_accuracy:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8s65SWQeSYWG",
        "outputId": "dd36fe33-b262-48fc-f051-c154497d2c43"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Segment: Training Data Accuracy\n",
            "Attempt 1: Accuracy score on training data: 0.96\n",
            "Attempt 2: Accuracy score on training data: 0.96\n",
            "Attempt 3: Accuracy score on training data: 0.96\n",
            "Attempt 4: Accuracy score on training data: 0.96\n",
            "Attempt 5: Accuracy score on training data: 0.96\n",
            "\n",
            "Segment: Test Data Accuracy\n",
            "Attempt 1: Accuracy score on test data: 1.00\n",
            "Attempt 2: Accuracy score on test data: 1.00\n",
            "Attempt 3: Accuracy score on test data: 1.00\n",
            "Attempt 4: Accuracy score on test data: 1.00\n",
            "Attempt 5: Accuracy score on test data: 1.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of training data : ', training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LKLnowsSg6u",
        "outputId": "11d2ac1d-b9b0-436b-bfc8-96b11b99c340"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of training data :  0.9625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Input data\n",
        "input_data = (\n",
        "    197.07600, 206.89600, 192.05500, 0.00289, 0.00001, 0.00166, 0.00168, 0.00498,\n",
        "    0.01098, 0.09700, 0.00563, 0.00680, 0.00802, 0.01689, 0.00339, 26.77500,\n",
        "    0.422229, 0.741367, -7.348300, 0.177551, 1.743867, 0.085569\n",
        ")\n",
        "\n",
        "# Step 1: Convert input data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# Step 2: Reshape the numpy array for a single sample (1 row, n features)\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)\n",
        "\n",
        "# Step 3: Handle feature mismatches\n",
        "expected_features = scaler.n_features_in_\n",
        "\n",
        "if input_data_reshaped.shape[1] > expected_features:\n",
        "    # Truncate input data if it has more features than expected\n",
        "    input_data_reshaped = input_data_reshaped[:, :expected_features]\n",
        "elif input_data_reshaped.shape[1] < expected_features:\n",
        "    # Pad input data with zeros if it has fewer features than expected\n",
        "    padding = np.zeros((1, expected_features - input_data_reshaped.shape[1]))\n",
        "    input_data_reshaped = np.hstack((input_data_reshaped, padding))\n",
        "\n",
        "# Step 4: Standardize the input data using the fitted scaler\n",
        "std_data = scaler.transform(input_data_reshaped)\n",
        "\n",
        "# Step 5: Predict using the trained model\n",
        "prediction = model.predict(std_data)\n",
        "\n",
        "# Step 6: Interpret and print the prediction\n",
        "if prediction[0] == 0:\n",
        "    print(\"Final Output: The Person does not have Parkinson's Disease.\")\n",
        "else:\n",
        "    print(\" Final Output: The Person has Parkinson's Disease.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0Dnk9yaSdFJ",
        "outputId": "44e88a95-a2f5-4ee8-d40a-8edf56ebee42"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Final Output: The Person has Parkinson's Disease.\n"
          ]
        }
      ]
    }
  ]
}